VMKeep provides always-on dedicated Apple Silicon machines—not virtual machines. Teams can standardize the chip, memory, and toolchain, turning MLX inference, mobile builds, and remote collaboration from temporary setups into repeatable workflows.
Tasks suited to a cloud Mac typically share three traits: they depend on macOS or Apple Silicon, run beyond local working hours, and need a fixed environment for reproducible results. For short local checks, your existing device may be enough.
Use SSH for command-line development and automation, and VNC for the macOS graphical interface. Your code, dependencies, and local services stay in place instead of requiring a fresh setup every day.
Keep the runner, Xcode, CocoaPods, Node.js, and dependency caches on the same physical node so every build runs with the same paths, versions, and permission boundaries.
Run MLX on a defined chip and memory configuration, recording the model quantization version, peak memory, time to first token, and sustained throughput to avoid result drift across team members’ devices.
There is no need to build complex orchestration for the first deployment. First verify locally that the model loads, requests return, and memory remains stable, then expose it to controlled clients. This separates model compatibility issues from network troubleshooting.
Record the model source, version, quantization method, context length, and file checksum. Keep the model directory separate from the service code so quantized versions can be replaced without changing the startup logic.
Pin Python and MLX dependency versions and commit the installation manifest to the repository. Start with a single command-line inference to verify the tokenizer, weight format, and memory requirements.
Have the service listen on the local address first, with health checks, request timeouts, and structured logs. Use fixed prompts to verify the response structure, then monitor time to first token and peak memory.
Once local requests are stable, open access according to your team’s network policy. Clients should retain timeout, retry, and request IDs; the service should record the model version and inference parameters.
curl -X POST http://127.0.0.1:8080/infer \
-H "Content-Type: application/json" \
-d '{
"prompt": "Summarize build log",
"max_tokens": 128
}'Do not record only one throughput number. Model version, quantization method, context length, concurrency, and warm-up rounds all affect the result. Freeze the test conditions first, then decide whether to change the model or upgrade the configuration.
| Record | Fixed conditions | Purpose |
|---|---|---|
| Quantization version | Same model and context length | Compare the trade-offs between accuracy, memory, and speed |
| Peak memory | Same inputs and concurrency | Determine whether the configuration has sufficient headroom for stable operation |
| Time to first token | Same warm-up state | Assess wait time for interactive requests |
| Sustained throughput | Same output length | Evaluate long-output and batch-task performance |
| Failure record | Retain request IDs and logs | Distinguish model, memory, service, and client issues |
The value of a fixed physical node is not merely that it can run Xcode. It gives source code, dependencies, tests, signing operations, and archive locations clear boundaries. When a build fails, you can trace the execution sequence instead of guessing at environment differences.
Use restricted credentials to access the specified repository, pinning the branch, commit, and submodule state.
Install dependencies from the lockfiles and isolate cache directories by project and version to prevent stale-cache contamination.
Specify the workspace, scheme, configuration, and destination, and save the complete command.
Record unit tests, UI tests, and failed cases separately, retaining machine-readable reports.
Restrict access to signing assets and never write certificate contents or sensitive credentials to build logs.
Store archives, logs, and test reports by commit and task ID, with a cleanup policy in place.
Identify the failed stage first, then decide whether to rerun tests, rebuild dependencies, or archive again. Use different retry strategies for cache, network, and code failures.
React Native builds span Node.js, package managers, CocoaPods, and Xcode. Pinning only JavaScript packages is not enough; Ruby, Pods, Xcode project settings, and build commands should also be part of the reproducibility checklist.
Pin the runtime and package manager versions, and separate caches by lockfile fingerprint.
Record the Pods state, scheme, build configuration, and target device.
Link the commit ID, build task, and artifact directory.
Manage JavaScript package, Pods, and Xcode derived-data caches separately. Cache hits can improve speed, but must not mask version mismatches.
Recommended records: lockfile summary, Pods state, Xcode versionAutomation scripts should read only the credentials required for execution. Redact sensitive content in logs and clean up temporary files after the build according to team policy.
Recommended to retain: commit ID, task ID, artifact checksumBoth connection methods can serve the same dedicated physical machine, but they have different purposes. After the first connection, update the password, configure SSH keys, and limit credential distribution. Use the console’s live status as the source of truth for connection details and availability.
Teams can assign different runners, model experiments, or build queues to separate physical nodes. Each node records its task scope, environment versions, and artifact directories independently, making troubleshooting and capacity planning more straightforward than with one shared mixed environment.
Dependency restoration, testing, and archiving run in a fixed build directory.
JavaScript, Pods, and Xcode derived-data caches are isolated.
Keep the chip, memory, model version, and input conditions fixed.
All three tiers are dedicated Apple Silicon machines, not virtual machines. Control costs for lightweight validation, prioritize caching and concurrency for continuous pipelines, and check peak usage after model loading for high-memory inference.
M4 · 16GB · 256GB
Ideal for single-project development, lightweight iOS builds, MLX environment validation, and small-scale model compatibility testing. Plan cache and artifact cleanup in advance when storage is tight.
M4 · 24GB · 512GB
Ideal for always-on runners, medium-project dependency caches, continuous React Native builds, and MLX inference experiments that need more memory headroom.
M4 Pro · 64GB · 2TB
Ideal for high-memory MLX inference, comparing quantized larger models, parallel experiments, and heavy build queues. Validate first with the actual model and concurrency parameters.
All three machine tiers are listed as orderable in these five locations. Check the console for live availability.
Choose a configuration, then select Singapore, Japan (Tokyo), South Korea (Seoul), Hong Kong, or US East, and set a daily, weekly, monthly, or quarterly rental term. Manage orders and machines in the console.